Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations4915
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory691.3 KiB
Average record size in memory144.0 B

Variable types

Numeric14
Categorical4

Alerts

annualHomeownersInsurance is highly overall correlated with bathrooms and 8 other fieldsHigh correlation
bathrooms is highly overall correlated with annualHomeownersInsurance and 4 other fieldsHigh correlation
bedrooms is highly overall correlated with annualHomeownersInsurance and 6 other fieldsHigh correlation
cluster is highly overall correlated with annualHomeownersInsurance and 11 other fieldsHigh correlation
cluster_average_price is highly overall correlated with annualHomeownersInsurance and 11 other fieldsHigh correlation
countyFIPS is highly overall correlated with cluster and 1 other fieldsHigh correlation
homeType_CONDO is highly overall correlated with annualHomeownersInsurance and 8 other fieldsHigh correlation
homeType_SINGLE_FAMILY is highly overall correlated with annualHomeownersInsurance and 5 other fieldsHigh correlation
latitude is highly overall correlated with cluster and 1 other fieldsHigh correlation
livingArea is highly overall correlated with annualHomeownersInsurance and 7 other fieldsHigh correlation
longitude is highly overall correlated with cluster and 1 other fieldsHigh correlation
monthlyHoaFee is highly overall correlated with homeType_CONDOHigh correlation
price is highly overall correlated with annualHomeownersInsurance and 8 other fieldsHigh correlation
propertyTaxRate is highly overall correlated with cluster and 1 other fieldsHigh correlation
rentZestimate is highly overall correlated with annualHomeownersInsurance and 7 other fieldsHigh correlation
zipcode is highly overall correlated with cluster and 1 other fieldsHigh correlation
monthlyHoaFee has 3943 (80.2%) zeros Zeros

Reproduction

Analysis started2024-12-17 00:39:16.993197
Analysis finished2024-12-17 00:39:41.888805
Duration24.9 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

longitude
Real number (ℝ)

High correlation 

Distinct3673
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-149.2102
Minimum-150.01093
Maximum-70.4831
Zeros0
Zeros (%)0.0%
Negative4915
Negative (%)100.0%
Memory size38.5 KiB
2024-12-17T02:39:42.015286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-150.01093
5-th percentile-149.95921
Q1-149.92895
median-149.87314
Q3-149.81379
95-th percentile-149.72975
Maximum-70.4831
Range79.52783
Interquartile range (IQR)0.115155

Descriptive statistics

Standard deviation6.5336835
Coefficient of variation (CV)-0.043788452
Kurtosis102.47482
Mean-149.2102
Median Absolute Deviation (MAD)0.05764
Skewness10.114499
Sum-733368.11
Variance42.68902
MonotonicityNot monotonic
2024-12-17T02:39:42.215297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-149.73212 13
 
0.3%
-149.94147 13
 
0.3%
-149.92009 12
 
0.2%
-149.89989 12
 
0.2%
-149.72975 11
 
0.2%
-149.82962 10
 
0.2%
-149.89291 8
 
0.2%
-149.88034 8
 
0.2%
-149.89194 7
 
0.1%
-149.9455 7
 
0.1%
Other values (3663) 4814
97.9%
ValueCountFrequency (%)
-150.01093 1
< 0.1%
-150.0097 1
< 0.1%
-150.00902 1
< 0.1%
-150.00879 1
< 0.1%
-150.00493 1
< 0.1%
-150.0028 1
< 0.1%
-150.00247 1
< 0.1%
-150.00201 1
< 0.1%
-150.00186 1
< 0.1%
-150.00182 1
< 0.1%
ValueCountFrequency (%)
-70.4831 1
< 0.1%
-70.483406 2
< 0.1%
-71.42977 1
< 0.1%
-72.25583 1
< 0.1%
-73.171455 1
< 0.1%
-73.702896 1
< 0.1%
-73.819725 1
< 0.1%
-73.893456 1
< 0.1%
-75.349976 1
< 0.1%
-75.40257 1
< 0.1%

countyFIPS
Real number (ℝ)

High correlation 

Distinct36
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2249.4529
Minimum2020
Maximum55079
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.5 KiB
2024-12-17T02:39:42.388802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2020
5-th percentile2020
Q12020
median2020
Q32020
95-th percentile2020
Maximum55079
Range53059
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2665.8739
Coefficient of variation (CV)1.185121
Kurtosis200.81946
Mean2249.4529
Median Absolute Deviation (MAD)0
Skewness13.610229
Sum11056061
Variance7106883.8
MonotonicityNot monotonic
2024-12-17T02:39:42.557392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2020 4864
99.0%
12015 4
 
0.1%
26125 3
 
0.1%
6065 3
 
0.1%
34005 2
 
< 0.1%
29095 2
 
< 0.1%
10005 2
 
< 0.1%
37071 2
 
< 0.1%
6071 2
 
< 0.1%
55079 2
 
< 0.1%
Other values (26) 29
 
0.6%
ValueCountFrequency (%)
2020 4864
99.0%
6059 1
 
< 0.1%
6065 3
 
0.1%
6071 2
 
< 0.1%
9001 2
 
< 0.1%
10005 2
 
< 0.1%
12011 1
 
< 0.1%
12015 4
 
0.1%
12021 1
 
< 0.1%
12071 1
 
< 0.1%
ValueCountFrequency (%)
55079 2
< 0.1%
48491 1
< 0.1%
48245 1
< 0.1%
42095 1
< 0.1%
42077 1
< 0.1%
42049 1
< 0.1%
40037 1
< 0.1%
39155 1
< 0.1%
37071 2
< 0.1%
37023 1
< 0.1%

monthlyHoaFee
Real number (ℝ)

High correlation  Zeros 

Distinct203
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.618922
Minimum0
Maximum873
Zeros3943
Zeros (%)80.2%
Negative0
Negative (%)0.0%
Memory size38.5 KiB
2024-12-17T02:39:42.699472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile346
Maximum873
Range873
Interquartile range (IQR)0

Descriptive statistics

Standard deviation118.04396
Coefficient of variation (CV)2.5321041
Kurtosis7.4911907
Mean46.618922
Median Absolute Deviation (MAD)0
Skewness2.7362757
Sum229132
Variance13934.377
MonotonicityNot monotonic
2024-12-17T02:39:42.885227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3943
80.2%
18 29
 
0.6%
25 28
 
0.6%
90 24
 
0.5%
300 22
 
0.4%
400 21
 
0.4%
115 20
 
0.4%
265 19
 
0.4%
272 17
 
0.3%
338 16
 
0.3%
Other values (193) 776
 
15.8%
ValueCountFrequency (%)
0 3943
80.2%
4 13
 
0.3%
6 5
 
0.1%
8 5
 
0.1%
10 2
 
< 0.1%
11 6
 
0.1%
12 14
 
0.3%
13 12
 
0.2%
15 11
 
0.2%
16 5
 
0.1%
ValueCountFrequency (%)
873 2
 
< 0.1%
830 1
 
< 0.1%
752 4
0.1%
691 1
 
< 0.1%
681 6
0.1%
664 1
 
< 0.1%
650 1
 
< 0.1%
634 9
0.2%
565 2
 
< 0.1%
563 1
 
< 0.1%

annualHomeownersInsurance
Real number (ℝ)

High correlation 

Distinct2064
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1680.0374
Minimum252
Maximum8646
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.5 KiB
2024-12-17T02:39:43.063033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum252
5-th percentile661
Q11252
median1631
Q31972
95-th percentile2877.6
Maximum8646
Range8394
Interquartile range (IQR)720

Descriptive statistics

Standard deviation722.06046
Coefficient of variation (CV)0.42978832
Kurtosis7.8647068
Mean1680.0374
Median Absolute Deviation (MAD)360
Skewness1.6992913
Sum8257384
Variance521371.31
MonotonicityNot monotonic
2024-12-17T02:39:43.221543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1630 14
 
0.3%
1633 12
 
0.2%
1743 11
 
0.2%
1341 11
 
0.2%
1691 11
 
0.2%
1733 11
 
0.2%
1696 10
 
0.2%
1677 10
 
0.2%
1762 9
 
0.2%
1310 9
 
0.2%
Other values (2054) 4807
97.8%
ValueCountFrequency (%)
252 1
< 0.1%
335 1
< 0.1%
366 1
< 0.1%
378 2
< 0.1%
382 1
< 0.1%
384 1
< 0.1%
388 1
< 0.1%
394 1
< 0.1%
397 1
< 0.1%
402 1
< 0.1%
ValueCountFrequency (%)
8646 1
< 0.1%
8004 1
< 0.1%
7710 1
< 0.1%
6539 1
< 0.1%
6274 1
< 0.1%
6273 1
< 0.1%
6143 1
< 0.1%
6071 1
< 0.1%
5982 1
< 0.1%
5830 1
< 0.1%

yearBuilt
Real number (ℝ)

Distinct89
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1976.0761
Minimum1900
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.5 KiB
2024-12-17T02:39:43.382509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1952
Q11969
median1977
Q31983
95-th percentile1997
Maximum2022
Range122
Interquartile range (IQR)14

Descriptive statistics

Standard deviation12.988204
Coefficient of variation (CV)0.0065727245
Kurtosis1.1505688
Mean1976.0761
Median Absolute Deviation (MAD)6
Skewness-0.12820139
Sum9712414
Variance168.69344
MonotonicityNot monotonic
2024-12-17T02:39:43.544221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1983 464
 
9.4%
1982 339
 
6.9%
1978 235
 
4.8%
1975 208
 
4.2%
1984 197
 
4.0%
1974 190
 
3.9%
1977 182
 
3.7%
1972 181
 
3.7%
1981 167
 
3.4%
1976 162
 
3.3%
Other values (79) 2590
52.7%
ValueCountFrequency (%)
1900 2
 
< 0.1%
1911 1
 
< 0.1%
1915 1
 
< 0.1%
1930 1
 
< 0.1%
1935 1
 
< 0.1%
1938 2
 
< 0.1%
1939 3
0.1%
1940 5
0.1%
1941 7
0.1%
1942 4
0.1%
ValueCountFrequency (%)
2022 1
 
< 0.1%
2021 2
 
< 0.1%
2020 13
0.3%
2019 6
0.1%
2018 6
0.1%
2017 2
 
< 0.1%
2016 2
 
< 0.1%
2015 3
 
0.1%
2013 2
 
< 0.1%
2012 2
 
< 0.1%

latitude
Real number (ℝ)

High correlation 

Distinct3992
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.915614
Minimum26.004696
Maximum61.231228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.5 KiB
2024-12-17T02:39:43.682626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum26.004696
5-th percentile61.123306
Q161.14649
median61.172867
Q361.197457
95-th percentile61.219247
Maximum61.231228
Range35.226532
Interquartile range (IQR)0.0509665

Descriptive statistics

Standard deviation2.5643447
Coefficient of variation (CV)0.042096672
Kurtosis110.07436
Mean60.915614
Median Absolute Deviation (MAD)0.025723
Skewness-10.362467
Sum299400.24
Variance6.5758635
MonotonicityNot monotonic
2024-12-17T02:39:43.827616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.18444 13
 
0.3%
61.21068 12
 
0.2%
61.180492 12
 
0.2%
61.222507 11
 
0.2%
61.1795 10
 
0.2%
61.202625 10
 
0.2%
61.20454 10
 
0.2%
61.182777 9
 
0.2%
61.20415 9
 
0.2%
61.219677 7
 
0.1%
Other values (3982) 4812
97.9%
ValueCountFrequency (%)
26.004696 1
< 0.1%
26.10778 1
< 0.1%
26.527695 1
< 0.1%
26.899212 1
< 0.1%
27.510103 1
< 0.1%
28.249554 1
< 0.1%
28.95348 1
< 0.1%
29.595736 1
< 0.1%
29.986279 1
< 0.1%
30.6175 1
< 0.1%
ValueCountFrequency (%)
61.231228 1
< 0.1%
61.2311 1
< 0.1%
61.231094 1
< 0.1%
61.23106 1
< 0.1%
61.23081 1
< 0.1%
61.2308 1
< 0.1%
61.23066 1
< 0.1%
61.23053 1
< 0.1%
61.2305 1
< 0.1%
61.23045 1
< 0.1%

rentZestimate
Real number (ℝ)

High correlation 

Distinct2165
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2647.7137
Minimum782
Maximum7545
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.5 KiB
2024-12-17T02:39:43.959304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum782
5-th percentile1595.4
Q12181
median2609
Q33015
95-th percentile3928.5
Maximum7545
Range6763
Interquartile range (IQR)834

Descriptive statistics

Standard deviation721.58966
Coefficient of variation (CV)0.27253311
Kurtosis1.893501
Mean2647.7137
Median Absolute Deviation (MAD)418
Skewness0.83486308
Sum13013513
Variance520691.63
MonotonicityNot monotonic
2024-12-17T02:39:44.101658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2804 12
 
0.2%
1646 11
 
0.2%
1645 11
 
0.2%
2594 10
 
0.2%
1744 10
 
0.2%
1601 10
 
0.2%
1565 9
 
0.2%
1909 9
 
0.2%
2369 9
 
0.2%
1824 9
 
0.2%
Other values (2155) 4815
98.0%
ValueCountFrequency (%)
782 1
< 0.1%
846 1
< 0.1%
903 1
< 0.1%
1046 1
< 0.1%
1083 1
< 0.1%
1089 1
< 0.1%
1110 1
< 0.1%
1125 1
< 0.1%
1174 1
< 0.1%
1175 1
< 0.1%
ValueCountFrequency (%)
7545 1
< 0.1%
6515 1
< 0.1%
6253 1
< 0.1%
6072 1
< 0.1%
6023 1
< 0.1%
5999 1
< 0.1%
5785 1
< 0.1%
5783 1
< 0.1%
5734 1
< 0.1%
5658 1
< 0.1%

timeOnZillow
Real number (ℝ)

Distinct2473
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3788.3434
Minimum1
Maximum19949
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.5 KiB
2024-12-17T02:39:44.239994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1336
Q13179
median3781
Q34398.5
95-th percentile6308
Maximum19949
Range19948
Interquartile range (IQR)1219.5

Descriptive statistics

Standard deviation1563.7836
Coefficient of variation (CV)0.41278822
Kurtosis8.6106925
Mean3788.3434
Median Absolute Deviation (MAD)614
Skewness1.2218956
Sum18619708
Variance2445419
MonotonicityNot monotonic
2024-12-17T02:39:44.397388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3917 15
 
0.3%
3758 14
 
0.3%
3581 14
 
0.3%
3644 13
 
0.3%
3728 12
 
0.2%
3666 11
 
0.2%
4085 11
 
0.2%
3532 11
 
0.2%
4031 10
 
0.2%
3882 10
 
0.2%
Other values (2463) 4794
97.5%
ValueCountFrequency (%)
1 1
 
< 0.1%
4 1
 
< 0.1%
5 3
0.1%
8 1
 
< 0.1%
9 2
< 0.1%
10 1
 
< 0.1%
11 3
0.1%
12 3
0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
19949 3
0.1%
11640 1
 
< 0.1%
11152 1
 
< 0.1%
11065 1
 
< 0.1%
10994 1
 
< 0.1%
10777 1
 
< 0.1%
10766 1
 
< 0.1%
10759 1
 
< 0.1%
10701 1
 
< 0.1%
10575 1
 
< 0.1%

livingArea
Real number (ℝ)

High correlation 

Distinct1851
Distinct (%)37.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1810.1776
Minimum20
Maximum8349
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.5 KiB
2024-12-17T02:39:44.576634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile746
Q11178.5
median1720
Q32150.5
95-th percentile3505.1
Maximum8349
Range8329
Interquartile range (IQR)972

Descriptive statistics

Standard deviation872.95082
Coefficient of variation (CV)0.48224595
Kurtosis4.7383456
Mean1810.1776
Median Absolute Deviation (MAD)498
Skewness1.6027467
Sum8897023
Variance762043.14
MonotonicityNot monotonic
2024-12-17T02:39:44.695497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1040 67
 
1.4%
1920 51
 
1.0%
1824 47
 
1.0%
1872 43
 
0.9%
1728 39
 
0.8%
1976 32
 
0.7%
1200 31
 
0.6%
988 27
 
0.5%
1152 26
 
0.5%
1344 23
 
0.5%
Other values (1841) 4529
92.1%
ValueCountFrequency (%)
20 1
< 0.1%
320 1
< 0.1%
399 2
< 0.1%
400 1
< 0.1%
404 1
< 0.1%
415 1
< 0.1%
446 1
< 0.1%
450 1
< 0.1%
460 1
< 0.1%
470 1
< 0.1%
ValueCountFrequency (%)
8349 1
< 0.1%
7500 1
< 0.1%
7227 1
< 0.1%
7010 2
< 0.1%
7004 1
< 0.1%
6984 1
< 0.1%
6878 1
< 0.1%
6692 1
< 0.1%
6477 1
< 0.1%
6451 1
< 0.1%

zipcode
Real number (ℝ)

High correlation 

Distinct58
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98930.938
Minimum2649
Maximum99518
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.5 KiB
2024-12-17T02:39:44.835899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2649
5-th percentile99501
Q199502
median99507
Q399515
95-th percentile99518
Maximum99518
Range96869
Interquartile range (IQR)13

Descriptive statistics

Standard deviation6281.3839
Coefficient of variation (CV)0.063492615
Kurtosis148.56915
Mean98930.938
Median Absolute Deviation (MAD)5
Skewness-11.917762
Sum4.8624556 × 108
Variance39455784
MonotonicityNot monotonic
2024-12-17T02:39:44.982435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99507 900
18.3%
99502 833
16.9%
99508 736
15.0%
99517 606
12.3%
99504 405
8.2%
99518 393
8.0%
99501 347
 
7.1%
99515 248
 
5.0%
99503 240
 
4.9%
99516 156
 
3.2%
Other values (48) 51
 
1.0%
ValueCountFrequency (%)
2649 3
0.1%
2865 1
 
< 0.1%
6415 1
 
< 0.1%
6607 1
 
< 0.1%
11355 1
 
< 0.1%
12118 1
 
< 0.1%
12524 1
 
< 0.1%
16509 1
 
< 0.1%
18015 1
 
< 0.1%
18018 1
 
< 0.1%
ValueCountFrequency (%)
99518 393
8.0%
99517 606
12.3%
99516 156
 
3.2%
99515 248
 
5.0%
99508 736
15.0%
99507 900
18.3%
99504 405
8.2%
99503 240
 
4.9%
99502 833
16.9%
99501 347
 
7.1%

propertyTaxRate
Real number (ℝ)

High correlation 

Distinct45
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.308059
Minimum0.57
Maximum1.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.5 KiB
2024-12-17T02:39:45.144977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.57
5-th percentile1.31
Q11.31
median1.31
Q31.31
95-th percentile1.31
Maximum1.89
Range1.32
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.044278421
Coefficient of variation (CV)0.033850477
Kurtosis180.49494
Mean1.308059
Median Absolute Deviation (MAD)0
Skewness-9.4320429
Sum6429.11
Variance0.0019605785
MonotonicityNot monotonic
2024-12-17T02:39:45.278706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1.31 4864
99.0%
0.63 3
 
0.1%
1.36 2
 
< 0.1%
1.57 2
 
< 0.1%
1.14 2
 
< 0.1%
0.66 2
 
< 0.1%
0.59 2
 
< 0.1%
1.66 1
 
< 0.1%
0.91 1
 
< 0.1%
1.24 1
 
< 0.1%
Other values (35) 35
 
0.7%
ValueCountFrequency (%)
0.57 1
 
< 0.1%
0.59 2
< 0.1%
0.61 1
 
< 0.1%
0.62 1
 
< 0.1%
0.63 3
0.1%
0.66 2
< 0.1%
0.67 1
 
< 0.1%
0.71 1
 
< 0.1%
0.72 1
 
< 0.1%
0.73 1
 
< 0.1%
ValueCountFrequency (%)
1.89 1
< 0.1%
1.87 1
< 0.1%
1.85 1
< 0.1%
1.72 1
< 0.1%
1.69 1
< 0.1%
1.66 1
< 0.1%
1.57 2
< 0.1%
1.56 1
< 0.1%
1.52 1
< 0.1%
1.51 1
< 0.1%

bathrooms
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0909664
Minimum0
Maximum10
Zeros22
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size38.5 KiB
2024-12-17T02:39:45.405815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.5
median2
Q32.5
95-th percentile3.5
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.85312086
Coefficient of variation (CV)0.40800314
Kurtosis4.1007024
Mean2.0909664
Median Absolute Deviation (MAD)0.5
Skewness1.1306375
Sum10277.1
Variance0.7278152
MonotonicityNot monotonic
2024-12-17T02:39:45.509578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2 1953
39.7%
1 925
18.8%
3 669
 
13.6%
2.5 580
 
11.8%
1.5 412
 
8.4%
4 132
 
2.7%
3.5 95
 
1.9%
5 32
 
0.7%
4.5 24
 
0.5%
0 22
 
0.4%
Other values (13) 71
 
1.4%
ValueCountFrequency (%)
0 22
 
0.4%
0.5 10
 
0.2%
1 925
18.8%
1.3 1
 
< 0.1%
1.5 412
 
8.4%
1.75 15
 
0.3%
1.8 1
 
< 0.1%
2 1953
39.7%
2.25 3
 
0.1%
2.5 580
 
11.8%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 1
 
< 0.1%
7 3
 
0.1%
6.5 1
 
< 0.1%
6 11
 
0.2%
5.5 8
 
0.2%
5 32
 
0.7%
4.5 24
 
0.5%
4 132
2.7%
3.75 2
 
< 0.1%

bedrooms
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.202645
Minimum0
Maximum14
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size38.5 KiB
2024-12-17T02:39:45.614038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1627936
Coefficient of variation (CV)0.36307291
Kurtosis5.2726558
Mean3.202645
Median Absolute Deviation (MAD)1
Skewness1.243081
Sum15741
Variance1.3520891
MonotonicityNot monotonic
2024-12-17T02:39:45.721715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 2086
42.4%
4 1202
24.5%
2 985
20.0%
5 245
 
5.0%
1 196
 
4.0%
6 138
 
2.8%
7 25
 
0.5%
8 14
 
0.3%
10 11
 
0.2%
0 7
 
0.1%
Other values (2) 6
 
0.1%
ValueCountFrequency (%)
0 7
 
0.1%
1 196
 
4.0%
2 985
20.0%
3 2086
42.4%
4 1202
24.5%
5 245
 
5.0%
6 138
 
2.8%
7 25
 
0.5%
8 14
 
0.3%
9 5
 
0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
10 11
 
0.2%
9 5
 
0.1%
8 14
 
0.3%
7 25
 
0.5%
6 138
 
2.8%
5 245
 
5.0%
4 1202
24.5%
3 2086
42.4%
2 985
20.0%

price
Real number (ℝ)

High correlation 

Distinct3178
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean400004.42
Minimum60000
Maximum2058500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.5 KiB
2024-12-17T02:39:45.853311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum60000
5-th percentile157300
Q1298150
median388300
Q3469500
95-th percentile685190
Maximum2058500
Range1998500
Interquartile range (IQR)171350

Descriptive statistics

Standard deviation171919
Coefficient of variation (CV)0.42979276
Kurtosis7.8643862
Mean400004.42
Median Absolute Deviation (MAD)85800
Skewness1.6992632
Sum1.9660217 × 109
Variance2.9556144 × 1010
MonotonicityNot monotonic
2024-12-17T02:39:46.005933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
388100 7
 
0.1%
419500 6
 
0.1%
403900 6
 
0.1%
375900 6
 
0.1%
420900 6
 
0.1%
462800 6
 
0.1%
411600 6
 
0.1%
394000 6
 
0.1%
451800 5
 
0.1%
388800 5
 
0.1%
Other values (3168) 4856
98.8%
ValueCountFrequency (%)
60000 1
< 0.1%
79700 1
< 0.1%
87100 1
< 0.1%
90100 2
< 0.1%
91000 1
< 0.1%
91500 1
< 0.1%
92400 1
< 0.1%
93800 1
< 0.1%
94600 1
< 0.1%
95600 1
< 0.1%
ValueCountFrequency (%)
2058500 1
< 0.1%
1905800 1
< 0.1%
1835600 1
< 0.1%
1556900 1
< 0.1%
1493700 1
< 0.1%
1493600 1
< 0.1%
1462700 1
< 0.1%
1445400 1
< 0.1%
1424300 1
< 0.1%
1388100 1
< 0.1%

homeType_CONDO
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.5 KiB
0
4178 
1
737 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4915
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 4178
85.0%
1 737
 
15.0%

Length

2024-12-17T02:39:46.145530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-17T02:39:46.233462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4178
85.0%
1 737
 
15.0%

Most occurring characters

ValueCountFrequency (%)
0 4178
85.0%
1 737
 
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4915
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4178
85.0%
1 737
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4915
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4178
85.0%
1 737
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4915
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4178
85.0%
1 737
 
15.0%

homeType_SINGLE_FAMILY
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.5 KiB
1
3605 
0
1310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4915
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 3605
73.3%
0 1310
 
26.7%

Length

2024-12-17T02:39:46.335598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-17T02:39:46.391232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3605
73.3%
0 1310
 
26.7%

Most occurring characters

ValueCountFrequency (%)
1 3605
73.3%
0 1310
 
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4915
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3605
73.3%
0 1310
 
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4915
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3605
73.3%
0 1310
 
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4915
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3605
73.3%
0 1310
 
26.7%

cluster
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.5 KiB
3
3057 
0
1013 
1
794 
2
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4915
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
3 3057
62.2%
0 1013
 
20.6%
1 794
 
16.2%
2 51
 
1.0%

Length

2024-12-17T02:39:46.469346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-17T02:39:46.563045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 3057
62.2%
0 1013
 
20.6%
1 794
 
16.2%
2 51
 
1.0%

Most occurring characters

ValueCountFrequency (%)
3 3057
62.2%
0 1013
 
20.6%
1 794
 
16.2%
2 51
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4915
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 3057
62.2%
0 1013
 
20.6%
1 794
 
16.2%
2 51
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4915
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 3057
62.2%
0 1013
 
20.6%
1 794
 
16.2%
2 51
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4915
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 3057
62.2%
0 1013
 
20.6%
1 794
 
16.2%
2 51
 
1.0%

cluster_average_price
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.5 KiB
377817.8976789801
3057 
624584.1379310344
1013 
200110.15355805244
794 
422504.4117647059
 
51

Length

Max length18
Median length17
Mean length17.161546
Min length17

Characters and Unicode

Total characters84349
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row624584.1379310344
2nd row624584.1379310344
3rd row624584.1379310344
4th row624584.1379310344
5th row624584.1379310344

Common Values

ValueCountFrequency (%)
377817.8976789801 3057
62.2%
624584.1379310344 1013
 
20.6%
200110.15355805244 794
 
16.2%
422504.4117647059 51
 
1.0%

Length

2024-12-17T02:39:46.680791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-17T02:39:46.768751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
377817.8976789801 3057
62.2%
624584.1379310344 1013
 
20.6%
200110.15355805244 794
 
16.2%
422504.4117647059 51
 
1.0%

Most occurring characters

ValueCountFrequency (%)
7 16400
19.4%
8 14035
16.6%
1 10624
12.6%
0 7348
8.7%
9 7178
8.5%
3 6890
8.2%
4 5844
 
6.9%
. 4915
 
5.8%
5 4291
 
5.1%
6 4121
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84349
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 16400
19.4%
8 14035
16.6%
1 10624
12.6%
0 7348
8.7%
9 7178
8.5%
3 6890
8.2%
4 5844
 
6.9%
. 4915
 
5.8%
5 4291
 
5.1%
6 4121
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84349
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 16400
19.4%
8 14035
16.6%
1 10624
12.6%
0 7348
8.7%
9 7178
8.5%
3 6890
8.2%
4 5844
 
6.9%
. 4915
 
5.8%
5 4291
 
5.1%
6 4121
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84349
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 16400
19.4%
8 14035
16.6%
1 10624
12.6%
0 7348
8.7%
9 7178
8.5%
3 6890
8.2%
4 5844
 
6.9%
. 4915
 
5.8%
5 4291
 
5.1%
6 4121
 
4.9%

Interactions

2024-12-17T02:39:39.611566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:17.916169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:19.566384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:21.056673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:22.633965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:24.959183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:26.464492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:27.989046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:29.470898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:31.286793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:33.260406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:34.905673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:36.622559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:38.144155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:39.716095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:18.063211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:19.683205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:21.189320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:23.690942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:25.067527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:26.649127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:28.112871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:29.591074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:31.755443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:33.378013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:35.022251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:36.752513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:38.277051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:39.821024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:18.188442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:19.799794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:21.288916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:23.786453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:25.164026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:26.738111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:28.199198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:29.726380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:31.887788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:33.504843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:35.122793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:36.873111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:38.377970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:39.913311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:18.304777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:19.894887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:21.391649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:23.892054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:25.242556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:26.835844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:28.294196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:29.846636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:31.987332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:33.622781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:35.234213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:36.988296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:38.479028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:39.996195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:18.422000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:19.983130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:21.517152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:23.985496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:25.338969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:26.953963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:28.395498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:29.941883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:32.105959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:33.735594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:35.339438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:37.083527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:38.559042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:40.099358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:18.556475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:20.099687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:21.633715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:24.090025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:25.432822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:27.067016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:28.508213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:30.090683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:32.250040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:33.855608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:35.443851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:37.176170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:38.653550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:40.196875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:18.700271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:20.199496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:21.750781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:24.181282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:25.552857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:27.163263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:28.612530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:30.219209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:32.364571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:33.989999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:35.575815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:37.267123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:38.765239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:40.308308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:18.815863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:20.316259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:21.850430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:24.290276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:25.665611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:27.249961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:28.699248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:30.305748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:32.470281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:34.105798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:35.772530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:37.365371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:38.879602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:40.405147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:18.933111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:20.427323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:21.984285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:24.387933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:25.761334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:27.364981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:28.815328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:30.456202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:32.585618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:34.223278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:35.922346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:37.455939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:38.990209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:40.499944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:19.025722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:20.514861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:22.100611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:24.482650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:25.862954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:27.449812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:28.917551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:30.570755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:32.688231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:34.338118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:36.039377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:37.535460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:39.078957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:40.612773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:19.130424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:20.599577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:22.213477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:24.577291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:25.983274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:27.555568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:29.039517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:30.687297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:32.775433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:34.444215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:36.139227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:37.653129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:39.190545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:40.707261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:19.233051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:20.706750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:22.330698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:24.657833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:26.102277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:27.666929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:29.139716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:30.840369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:32.916449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:34.556388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:36.272560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:37.822481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:39.292476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:40.801465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:19.333236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:20.819953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:22.434037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:24.752674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:26.207426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:27.769347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:29.234046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:31.020875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:33.009436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:34.655822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:36.389016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:37.920649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:39.387300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:40.909232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:19.456705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:20.938089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:22.542603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:24.847996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:26.337285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:27.873211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:29.340753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:31.150638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:33.119330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:34.756011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:36.499907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:38.020993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-17T02:39:39.476934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-12-17T02:39:46.860628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
annualHomeownersInsurancebathroomsbedroomsclustercluster_average_pricecountyFIPShomeType_CONDOhomeType_SINGLE_FAMILYlatitudelivingArealongitudemonthlyHoaFeepricepropertyTaxRaterentZestimatetimeOnZillowyearBuiltzipcode
annualHomeownersInsurance1.0000.7110.6620.6170.617-0.0290.7070.550-0.2870.880-0.052-0.2551.0000.0040.839-0.0230.071-0.017
bathrooms0.7111.0000.5910.4380.4380.0060.2790.208-0.2250.7540.007-0.0570.711-0.0030.762-0.0540.2270.024
bedrooms0.6620.5911.0000.4430.443-0.0410.5640.538-0.1180.7290.033-0.2730.6620.0350.6700.000-0.0370.025
cluster0.6170.4380.4431.0001.0000.5400.9440.7330.5760.5330.5770.3550.6170.5400.5270.0850.3760.553
cluster_average_price0.6170.4380.4431.0001.0000.5400.9440.7330.5760.5330.5770.3550.6170.5400.5270.0850.3760.553
countyFIPS-0.0290.006-0.0410.5400.5401.0000.0000.000-0.176-0.0100.1760.022-0.029-0.292-0.048-0.0940.065-0.177
homeType_CONDO0.7070.2790.5640.9440.9440.0001.0000.6960.0130.5300.0000.6020.7070.0000.5500.0280.3430.000
homeType_SINGLE_FAMILY0.5500.2080.5380.7330.7330.0000.6961.0000.0000.3860.0220.4810.5500.0300.4940.0530.2630.000
latitude-0.287-0.225-0.1180.5760.576-0.1760.0130.0001.000-0.1970.177-0.034-0.2870.052-0.2990.045-0.345-0.046
livingArea0.8800.7540.7290.5330.533-0.0100.5300.386-0.1971.0000.019-0.2100.8800.0140.8220.0080.015-0.019
longitude-0.0520.0070.0330.5770.5770.1760.0000.0220.1770.0191.000-0.024-0.052-0.052-0.0610.0460.0490.010
monthlyHoaFee-0.255-0.057-0.2730.3550.3550.0220.6020.481-0.034-0.210-0.0241.000-0.255-0.012-0.182-0.0570.153-0.003
price1.0000.7110.6620.6170.617-0.0290.7070.550-0.2870.880-0.052-0.2551.0000.0040.839-0.0230.071-0.017
propertyTaxRate0.004-0.0030.0350.5400.540-0.2920.0000.0300.0520.014-0.052-0.0120.0041.0000.0090.032-0.0130.052
rentZestimate0.8390.7620.6700.5270.527-0.0480.5500.494-0.2990.822-0.061-0.1820.8390.0091.000-0.0150.127-0.006
timeOnZillow-0.023-0.0540.0000.0850.085-0.0940.0280.0530.0450.0080.046-0.057-0.0230.032-0.0151.000-0.0360.019
yearBuilt0.0710.227-0.0370.3760.3760.0650.3430.263-0.3450.0150.0490.1530.071-0.0130.127-0.0361.000-0.005
zipcode-0.0170.0240.0250.5530.553-0.1770.0000.000-0.046-0.0190.010-0.003-0.0170.052-0.0060.019-0.0051.000

Missing values

2024-12-17T02:39:41.072575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-17T02:39:41.757811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

longitudecountyFIPSmonthlyHoaFeeannualHomeownersInsuranceyearBuiltlatituderentZestimatetimeOnZillowlivingAreazipcodepropertyTaxRatebathroomsbedroomspricehomeType_CONDOhomeType_SINGLE_FAMILYclustercluster_average_price
0-149.908072020.00.028401959.061.2173083142.03609.02668.0995011.312.03.0676100010624584.137931
1-149.908222020.00.029341961.061.2171363113.04334.03179.0995011.312.03.0698600010624584.137931
2-149.908332020.00.041871983.061.2170004282.03758.03059.0995011.313.04.0996800010624584.137931
3-149.908342020.00.029201947.061.2167203458.03543.01642.0995011.312.05.0695300010624584.137931
4-149.907492020.00.041002000.061.2171204161.03953.04483.0995011.314.04.0976100100624584.137931
5-149.907232020.00.025352018.061.2170033943.03011.02560.0995011.313.53.0603600100624584.137931
6-149.907232020.00.030421961.061.2171403318.01512.03224.0995011.313.06.0724400000624584.137931
7-149.905462020.00.018651978.061.2183303591.01201.02087.0995011.313.02.0444100101200110.153558
8-149.910572020.00.08621973.061.2145201945.05089.0899.0995011.311.02.0205200101200110.153558
9-149.910372020.00.019441930.061.2153052128.03672.0678.0995011.311.01.0462800013377817.897679
longitudecountyFIPSmonthlyHoaFeeannualHomeownersInsuranceyearBuiltlatituderentZestimatetimeOnZillowlivingAreazipcodepropertyTaxRatebathroomsbedroomspricehomeType_CONDOhomeType_SINGLE_FAMILYclustercluster_average_price
4905-149.778442020.028.023141974.061.1147543658.04243.02688.0995161.312.04.0550900010624584.137931
4906-149.780172020.028.019331974.061.1147303427.03875.01872.0995161.312.03.0460300013377817.897679
4907-149.787952020.028.018351974.061.1155783172.04057.01789.0995161.312.03.0436800013377817.897679
4908-149.788712020.028.018031974.061.1142852845.03903.01496.0995161.312.53.0429400013377817.897679
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